This module defines functions and classes which implement a flexible error logging system for applications.
Logging is performed by calling methods on instances of the Logger class (hereafter called loggers). Each instance has a name, and they are conceptually arranged in a namespace hierarchy using dots (periods) as separators. For example, a logger named “scan” is the parent of loggers “scan.text”, “scan.html” and “scan.pdf”. Logger names can be anything you want, and indicate the area of an application in which a logged message originates.
Logged messages also have levels of importance associated with them. The default levels provided are DEBUG, INFO, WARNING, ERROR and CRITICAL. As a convenience, you indicate the importance of a logged message by calling an appropriate method of Logger. The methods are debug(), info(), warning(), error() and critical(), which mirror the default levels. You are not constrained to use these levels: you can specify your own and use a more general Logger method, log(), which takes an explicit level argument.
The key benefit of having the logging API provided by a standard library module is that all Python modules can participate in logging, so your application log can include messages from third-party modules.
It is, of course, possible to log messages with different verbosity levels or to different destinations. Support for writing log messages to files, HTTP GET/POST locations, email via SMTP, generic sockets, or OS-specific logging mechanisms are all supported by the standard module. You can also create your own log destination class if you have special requirements not met by any of the built-in classes.
Most applications are probably going to want to log to a file, so let’s start with that case. Using the basicConfig() function, we can set up the default handler so that debug messages are written to a file (in the example, we assume that you have the appropriate permissions to create a file called example.log in the current directory):
import logging
LOG_FILENAME = 'example.log'
logging.basicConfig(filename=LOG_FILENAME,level=logging.DEBUG)
logging.debug('This message should go to the log file')
And now if we open the file and look at what we have, we should find the log message:
DEBUG:root:This message should go to the log file
If you run the script repeatedly, the additional log messages are appended to the file. To create a new file each time, you can pass a filemode argument to basicConfig() with a value of 'w'. Rather than managing the file size yourself, though, it is simpler to use a RotatingFileHandler:
import glob
import logging
import logging.handlers
LOG_FILENAME = 'logging_rotatingfile_example.out'
# Set up a specific logger with our desired output level
my_logger = logging.getLogger('MyLogger')
my_logger.setLevel(logging.DEBUG)
# Add the log message handler to the logger
handler = logging.handlers.RotatingFileHandler(
LOG_FILENAME, maxBytes=20, backupCount=5)
my_logger.addHandler(handler)
# Log some messages
for i in range(20):
my_logger.debug('i = %d' % i)
# See what files are created
logfiles = glob.glob('%s*' % LOG_FILENAME)
for filename in logfiles:
print(filename)
The result should be 6 separate files, each with part of the log history for the application:
logging_rotatingfile_example.out
logging_rotatingfile_example.out.1
logging_rotatingfile_example.out.2
logging_rotatingfile_example.out.3
logging_rotatingfile_example.out.4
logging_rotatingfile_example.out.5
The most current file is always logging_rotatingfile_example.out, and each time it reaches the size limit it is renamed with the suffix .1. Each of the existing backup files is renamed to increment the suffix (.1 becomes .2, etc.) and the .6 file is erased.
Obviously this example sets the log length much much too small as an extreme example. You would want to set maxBytes to an appropriate value.
Another useful feature of the logging API is the ability to produce different messages at different log levels. This allows you to instrument your code with debug messages, for example, but turning the log level down so that those debug messages are not written for your production system. The default levels are CRITICAL, ERROR, WARNING, INFO, DEBUG and NOTSET.
The logger, handler, and log message call each specify a level. The log message is only emitted if the handler and logger are configured to emit messages of that level or lower. For example, if a message is CRITICAL, and the logger is set to ERROR, the message is emitted. If a message is a WARNING, and the logger is set to produce only ERRORs, the message is not emitted:
import logging
import sys
LEVELS = {'debug': logging.DEBUG,
'info': logging.INFO,
'warning': logging.WARNING,
'error': logging.ERROR,
'critical': logging.CRITICAL}
if len(sys.argv) > 1:
level_name = sys.argv[1]
level = LEVELS.get(level_name, logging.NOTSET)
logging.basicConfig(level=level)
logging.debug('This is a debug message')
logging.info('This is an info message')
logging.warning('This is a warning message')
logging.error('This is an error message')
logging.critical('This is a critical error message')
Run the script with an argument like ‘debug’ or ‘warning’ to see which messages show up at different levels:
$ python logging_level_example.py debug
DEBUG:root:This is a debug message
INFO:root:This is an info message
WARNING:root:This is a warning message
ERROR:root:This is an error message
CRITICAL:root:This is a critical error message
$ python logging_level_example.py info
INFO:root:This is an info message
WARNING:root:This is a warning message
ERROR:root:This is an error message
CRITICAL:root:This is a critical error message
You will notice that these log messages all have root embedded in them. The logging module supports a hierarchy of loggers with different names. An easy way to tell where a specific log message comes from is to use a separate logger object for each of your modules. Each new logger “inherits” the configuration of its parent, and log messages sent to a logger include the name of that logger. Optionally, each logger can be configured differently, so that messages from different modules are handled in different ways. Let’s look at a simple example of how to log from different modules so it is easy to trace the source of the message:
import logging
logging.basicConfig(level=logging.WARNING)
logger1 = logging.getLogger('package1.module1')
logger2 = logging.getLogger('package2.module2')
logger1.warning('This message comes from one module')
logger2.warning('And this message comes from another module')
And the output:
$ python logging_modules_example.py
WARNING:package1.module1:This message comes from one module
WARNING:package2.module2:And this message comes from another module
There are many more options for configuring logging, including different log message formatting options, having messages delivered to multiple destinations, and changing the configuration of a long-running application on the fly using a socket interface. All of these options are covered in depth in the library module documentation.
The logging library takes a modular approach and offers the several categories of components: loggers, handlers, filters, and formatters. Loggers expose the interface that application code directly uses. Handlers send the log records to the appropriate destination. Filters provide a finer grained facility for determining which log records to send on to a handler. Formatters specify the layout of the resultant log record.
Logger objects have a threefold job. First, they expose several methods to application code so that applications can log messages at runtime. Second, logger objects determine which log messages to act upon based upon severity (the default filtering facility) or filter objects. Third, logger objects pass along relevant log messages to all interested log handlers.
The most widely used methods on logger objects fall into two categories: configuration and message sending.
With the logger object configured, the following methods create log messages:
getLogger() returns a reference to a logger instance with the specified name if it is provided, or root if not. The names are period-separated hierarchical structures. Multiple calls to getLogger() with the same name will return a reference to the same logger object. Loggers that are further down in the hierarchical list are children of loggers higher up in the list. For example, given a logger with a name of foo, loggers with names of foo.bar, foo.bar.baz, and foo.bam are all descendants of foo. Child loggers propagate messages up to the handlers associated with their ancestor loggers. Because of this, it is unnecessary to define and configure handlers for all the loggers an application uses. It is sufficient to configure handlers for a top-level logger and create child loggers as needed.
Handler objects are responsible for dispatching the appropriate log messages (based on the log messages’ severity) to the handler’s specified destination. Logger objects can add zero or more handler objects to themselves with an addHandler() method. As an example scenario, an application may want to send all log messages to a log file, all log messages of error or higher to stdout, and all messages of critical to an email address. This scenario requires three individual handlers where each handler is responsible for sending messages of a specific severity to a specific location.
The standard library includes quite a few handler types; this tutorial uses only StreamHandler and FileHandler in its examples.
There are very few methods in a handler for application developers to concern themselves with. The only handler methods that seem relevant for application developers who are using the built-in handler objects (that is, not creating custom handlers) are the following configuration methods:
Application code should not directly instantiate and use instances of Handler. Instead, the Handler class is a base class that defines the interface that all handlers should have and establishes some default behavior that child classes can use (or override).
Formatter objects configure the final order, structure, and contents of the log message. Unlike the base logging.Handler class, application code may instantiate formatter classes, although you could likely subclass the formatter if your application needs special behavior. The constructor takes two optional arguments: a message format string and a date format string. If there is no message format string, the default is to use the raw message. If there is no date format string, the default date format is:
%Y-%m-%d %H:%M:%S
with the milliseconds tacked on at the end.
The message format string uses %(<dictionary key>)s styled string substitution; the possible keys are documented in Formatter Objects.
The following message format string will log the time in a human-readable format, the severity of the message, and the contents of the message, in that order:
"%(asctime)s - %(levelname)s - %(message)s"
Formatters use a user-configurable function to convert the creation time of a record to a tuple. By default, time.localtime() is used; to change this for a particular formatter instance, set the converter attribute of the instance to a function with the same signature as time.localtime() or time.gmtime(). To change it for all formatters, for example if you want all logging times to be shown in GMT, set the converter attribute in the Formatter class (to time.gmtime for GMT display).
Programmers can configure logging either by creating loggers, handlers, and formatters explicitly in a main module with the configuration methods listed above (using Python code), or by creating a logging config file. The following code is an example of configuring a very simple logger, a console handler, and a simple formatter in a Python module:
import logging
# create logger
logger = logging.getLogger("simple_example")
logger.setLevel(logging.DEBUG)
# create console handler and set level to debug
ch = logging.StreamHandler()
ch.setLevel(logging.DEBUG)
# create formatter
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
# add formatter to ch
ch.setFormatter(formatter)
# add ch to logger
logger.addHandler(ch)
# "application" code
logger.debug("debug message")
logger.info("info message")
logger.warn("warn message")
logger.error("error message")
logger.critical("critical message")
Running this module from the command line produces the following output:
$ python simple_logging_module.py
2005-03-19 15:10:26,618 - simple_example - DEBUG - debug message
2005-03-19 15:10:26,620 - simple_example - INFO - info message
2005-03-19 15:10:26,695 - simple_example - WARNING - warn message
2005-03-19 15:10:26,697 - simple_example - ERROR - error message
2005-03-19 15:10:26,773 - simple_example - CRITICAL - critical message
The following Python module creates a logger, handler, and formatter nearly identical to those in the example listed above, with the only difference being the names of the objects:
import logging
import logging.config
logging.config.fileConfig("logging.conf")
# create logger
logger = logging.getLogger("simpleExample")
# "application" code
logger.debug("debug message")
logger.info("info message")
logger.warn("warn message")
logger.error("error message")
logger.critical("critical message")
Here is the logging.conf file:
[loggers]
keys=root,simpleExample
[handlers]
keys=consoleHandler
[formatters]
keys=simpleFormatter
[logger_root]
level=DEBUG
handlers=consoleHandler
[logger_simpleExample]
level=DEBUG
handlers=consoleHandler
qualname=simpleExample
propagate=0
[handler_consoleHandler]
class=StreamHandler
level=DEBUG
formatter=simpleFormatter
args=(sys.stdout,)
[formatter_simpleFormatter]
format=%(asctime)s - %(name)s - %(levelname)s - %(message)s
datefmt=
The output is nearly identical to that of the non-config-file-based example:
$ python simple_logging_config.py
2005-03-19 15:38:55,977 - simpleExample - DEBUG - debug message
2005-03-19 15:38:55,979 - simpleExample - INFO - info message
2005-03-19 15:38:56,054 - simpleExample - WARNING - warn message
2005-03-19 15:38:56,055 - simpleExample - ERROR - error message
2005-03-19 15:38:56,130 - simpleExample - CRITICAL - critical message
You can see that the config file approach has a few advantages over the Python code approach, mainly separation of configuration and code and the ability of noncoders to easily modify the logging properties.
When developing a library which uses logging, some consideration needs to be given to its configuration. If the using application does not use logging, and library code makes logging calls, then a one-off message “No handlers could be found for logger X.Y.Z” is printed to the console. This message is intended to catch mistakes in logging configuration, but will confuse an application developer who is not aware of logging by the library.
In addition to documenting how a library uses logging, a good way to configure library logging so that it does not cause a spurious message is to add a handler which does nothing. This avoids the message being printed, since a handler will be found: it just doesn’t produce any output. If the library user configures logging for application use, presumably that configuration will add some handlers, and if levels are suitably configured then logging calls made in library code will send output to those handlers, as normal.
A do-nothing handler can be simply defined as follows:
import logging
class NullHandler(logging.Handler):
def emit(self, record):
pass
An instance of this handler should be added to the top-level logger of the logging namespace used by the library. If all logging by a library foo is done using loggers with names matching “foo.x.y”, then the code:
import logging
h = NullHandler()
logging.getLogger("foo").addHandler(h)
should have the desired effect. If an organisation produces a number of libraries, then the logger name specified can be “orgname.foo” rather than just “foo”.
New in version 3.1: The NullHandler class was not present in previous versions, but is now included, so that it need not be defined in library code.
The numeric values of logging levels are given in the following table. These are primarily of interest if you want to define your own levels, and need them to have specific values relative to the predefined levels. If you define a level with the same numeric value, it overwrites the predefined value; the predefined name is lost.
| Level | Numeric value |
|---|---|
| CRITICAL | 50 |
| ERROR | 40 |
| WARNING | 30 |
| INFO | 20 |
| DEBUG | 10 |
| NOTSET | 0 |
Levels can also be associated with loggers, being set either by the developer or through loading a saved logging configuration. When a logging method is called on a logger, the logger compares its own level with the level associated with the method call. If the logger’s level is higher than the method call’s, no logging message is actually generated. This is the basic mechanism controlling the verbosity of logging output.
Logging messages are encoded as instances of the LogRecord class. When a logger decides to actually log an event, a LogRecord instance is created from the logging message.
Logging messages are subjected to a dispatch mechanism through the use of handlers, which are instances of subclasses of the Handler class. Handlers are responsible for ensuring that a logged message (in the form of a LogRecord) ends up in a particular location (or set of locations) which is useful for the target audience for that message (such as end users, support desk staff, system administrators, developers). Handlers are passed LogRecord instances intended for particular destinations. Each logger can have zero, one or more handlers associated with it (via the addHandler() method of Logger). In addition to any handlers directly associated with a logger, all handlers associated with all ancestors of the logger are called to dispatch the message (unless the propagate flag for a logger is set to a false value, at which point the passing to ancestor handlers stops).
Just as for loggers, handlers can have levels associated with them. A handler’s level acts as a filter in the same way as a logger’s level does. If a handler decides to actually dispatch an event, the emit() method is used to send the message to its destination. Most user-defined subclasses of Handler will need to override this emit().
In addition to the base Handler class, many useful subclasses are provided:
New in version 3.1.
The NullHandler class was not present in previous versions.
The NullHandler, StreamHandler and FileHandler classes are defined in the core logging package. The other handlers are defined in a sub- module, logging.handlers. (There is also another sub-module, logging.config, for configuration functionality.)
Logged messages are formatted for presentation through instances of the Formatter class. They are initialized with a format string suitable for use with the % operator and a dictionary.
For formatting multiple messages in a batch, instances of BufferingFormatter can be used. In addition to the format string (which is applied to each message in the batch), there is provision for header and trailer format strings.
When filtering based on logger level and/or handler level is not enough, instances of Filter can be added to both Logger and Handler instances (through their addFilter() method). Before deciding to process a message further, both loggers and handlers consult all their filters for permission. If any filter returns a false value, the message is not processed further.
The basic Filter functionality allows filtering by specific logger name. If this feature is used, messages sent to the named logger and its children are allowed through the filter, and all others dropped.
In addition to the classes described above, there are a number of module- level functions.
Return a logger with the specified name or, if name is None, return a logger which is the root logger of the hierarchy. If specified, the name is typically a dot-separated hierarchical name like “a”, “a.b” or “a.b.c.d”. Choice of these names is entirely up to the developer who is using logging.
All calls to this function with a given name return the same logger instance. This means that logger instances never need to be passed between different parts of an application.
Return either the standard Logger class, or the last class passed to setLoggerClass(). This function may be called from within a new class definition, to ensure that installing a customised Logger class will not undo customisations already applied by other code. For example:
class MyLogger(logging.getLoggerClass()):
# ... override behaviour here
Logs a message with level DEBUG on the root logger. The msg is the message format string, and the args are the arguments which are merged into msg using the string formatting operator. (Note that this means that you can use keywords in the format string, together with a single dictionary argument.)
There are two keyword arguments in kwargs which are inspected: exc_info which, if it does not evaluate as false, causes exception information to be added to the logging message. If an exception tuple (in the format returned by sys.exc_info()) is provided, it is used; otherwise, sys.exc_info() is called to get the exception information.
The other optional keyword argument is extra which can be used to pass a dictionary which is used to populate the __dict__ of the LogRecord created for the logging event with user-defined attributes. These custom attributes can then be used as you like. For example, they could be incorporated into logged messages. For example:
FORMAT = "%(asctime)-15s %(clientip)s %(user)-8s %(message)s"
logging.basicConfig(format=FORMAT)
d = {'clientip': '192.168.0.1', 'user': 'fbloggs'}
logging.warning("Protocol problem: %s", "connection reset", extra=d)
would print something like:
2006-02-08 22:20:02,165 192.168.0.1 fbloggs Protocol problem: connection reset
The keys in the dictionary passed in extra should not clash with the keys used by the logging system. (See the Formatter documentation for more information on which keys are used by the logging system.)
If you choose to use these attributes in logged messages, you need to exercise some care. In the above example, for instance, the Formatter has been set up with a format string which expects ‘clientip’ and ‘user’ in the attribute dictionary of the LogRecord. If these are missing, the message will not be logged because a string formatting exception will occur. So in this case, you always need to pass the extra dictionary with these keys.
While this might be annoying, this feature is intended for use in specialized circumstances, such as multi-threaded servers where the same code executes in many contexts, and interesting conditions which arise are dependent on this context (such as remote client IP address and authenticated user name, in the above example). In such circumstances, it is likely that specialized Formatters would be used with particular Handlers.
Does basic configuration for the logging system by creating a StreamHandler with a default Formatter and adding it to the root logger. The functions debug(), info(), warning(), error() and critical() will call basicConfig() automatically if no handlers are defined for the root logger.
This function does nothing if the root logger already has handlers configured for it.
The following keyword arguments are supported.
| Format | Description |
|---|---|
| filename | Specifies that a FileHandler be created, using the specified filename, rather than a StreamHandler. |
| filemode | Specifies the mode to open the file, if filename is specified (if filemode is unspecified, it defaults to ‘a’). |
| format | Use the specified format string for the handler. |
| datefmt | Use the specified date/time format. |
| level | Set the root logger level to the specified level. |
| stream | Use the specified stream to initialize the StreamHandler. Note that this argument is incompatible with ‘filename’ - if both are present, ‘stream’ is ignored. |
See also
Loggers have the following attributes and methods. Note that Loggers are never instantiated directly, but always through the module-level function logging.getLogger(name).
Sets the threshold for this logger to lvl. Logging messages which are less severe than lvl will be ignored. When a logger is created, the level is set to NOTSET (which causes all messages to be processed when the logger is the root logger, or delegation to the parent when the logger is a non-root logger). Note that the root logger is created with level WARNING.
The term “delegation to the parent” means that if a logger has a level of NOTSET, its chain of ancestor loggers is traversed until either an ancestor with a level other than NOTSET is found, or the root is reached.
If an ancestor is found with a level other than NOTSET, then that ancestor’s level is treated as the effective level of the logger where the ancestor search began, and is used to determine how a logging event is handled.
If the root is reached, and it has a level of NOTSET, then all messages will be processed. Otherwise, the root’s level will be used as the effective level.
Logs a message with level DEBUG on this logger. The msg is the message format string, and the args are the arguments which are merged into msg using the string formatting operator. (Note that this means that you can use keywords in the format string, together with a single dictionary argument.)
There are two keyword arguments in kwargs which are inspected: exc_info which, if it does not evaluate as false, causes exception information to be added to the logging message. If an exception tuple (in the format returned by sys.exc_info()) is provided, it is used; otherwise, sys.exc_info() is called to get the exception information.
The other optional keyword argument is extra which can be used to pass a dictionary which is used to populate the __dict__ of the LogRecord created for the logging event with user-defined attributes. These custom attributes can then be used as you like. For example, they could be incorporated into logged messages. For example:
FORMAT = "%(asctime)-15s %(clientip)s %(user)-8s %(message)s"
logging.basicConfig(format=FORMAT)
d = { 'clientip' : '192.168.0.1', 'user' : 'fbloggs' }
logger = logging.getLogger("tcpserver")
logger.warning("Protocol problem: %s", "connection reset", extra=d)
would print something like
2006-02-08 22:20:02,165 192.168.0.1 fbloggs Protocol problem: connection reset
The keys in the dictionary passed in extra should not clash with the keys used by the logging system. (See the Formatter documentation for more information on which keys are used by the logging system.)
If you choose to use these attributes in logged messages, you need to exercise some care. In the above example, for instance, the Formatter has been set up with a format string which expects ‘clientip’ and ‘user’ in the attribute dictionary of the LogRecord. If these are missing, the message will not be logged because a string formatting exception will occur. So in this case, you always need to pass the extra dictionary with these keys.
While this might be annoying, this feature is intended for use in specialized circumstances, such as multi-threaded servers where the same code executes in many contexts, and interesting conditions which arise are dependent on this context (such as remote client IP address and authenticated user name, in the above example). In such circumstances, it is likely that specialized Formatters would be used with particular Handlers.
The logging package provides a lot of flexibility, and its configuration can appear daunting. This section demonstrates that simple use of the logging package is possible.
The simplest example shows logging to the console:
import logging
logging.debug('A debug message')
logging.info('Some information')
logging.warning('A shot across the bows')
If you run the above script, you’ll see this:
WARNING:root:A shot across the bows
Because no particular logger was specified, the system used the root logger. The debug and info messages didn’t appear because by default, the root logger is configured to only handle messages with a severity of WARNING or above. The message format is also a configuration default, as is the output destination of the messages - sys.stderr. The severity level, the message format and destination can be easily changed, as shown in the example below:
import logging
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)s %(message)s',
filename='myapp.log',
filemode='w')
logging.debug('A debug message')
logging.info('Some information')
logging.warning('A shot across the bows')
The basicConfig() method is used to change the configuration defaults, which results in output (written to myapp.log) which should look something like the following:
2004-07-02 13:00:08,743 DEBUG A debug message
2004-07-02 13:00:08,743 INFO Some information
2004-07-02 13:00:08,743 WARNING A shot across the bows
This time, all messages with a severity of DEBUG or above were handled, and the format of the messages was also changed, and output went to the specified file rather than the console.
Formatting uses the old Python string formatting - see section Old String Formatting Operations. The format string takes the following common specifiers. For a complete list of specifiers, consult the Formatter documentation.
| Format | Description |
|---|---|
| %(name)s | Name of the logger (logging channel). |
| %(levelname)s | Text logging level for the message ('DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'). |
| %(asctime)s | Human-readable time when the LogRecord was created. By default this is of the form “2003-07-08 16:49:45,896” (the numbers after the comma are millisecond portion of the time). |
| %(message)s | The logged message. |
To change the date/time format, you can pass an additional keyword parameter, datefmt, as in the following:
import logging
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)-8s %(message)s',
datefmt='%a, %d %b %Y %H:%M:%S',
filename='/temp/myapp.log',
filemode='w')
logging.debug('A debug message')
logging.info('Some information')
logging.warning('A shot across the bows')
which would result in output like
Fri, 02 Jul 2004 13:06:18 DEBUG A debug message
Fri, 02 Jul 2004 13:06:18 INFO Some information
Fri, 02 Jul 2004 13:06:18 WARNING A shot across the bows
The date format string follows the requirements of strftime() - see the documentation for the time module.
If, instead of sending logging output to the console or a file, you’d rather use a file-like object which you have created separately, you can pass it to basicConfig() using the stream keyword argument. Note that if both stream and filename keyword arguments are passed, the stream argument is ignored.
Of course, you can put variable information in your output. To do this, simply have the message be a format string and pass in additional arguments containing the variable information, as in the following example:
import logging
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)-8s %(message)s',
datefmt='%a, %d %b %Y %H:%M:%S',
filename='/temp/myapp.log',
filemode='w')
logging.error('Pack my box with %d dozen %s', 5, 'liquor jugs')
which would result in
Wed, 21 Jul 2004 15:35:16 ERROR Pack my box with 5 dozen liquor jugs
Let’s say you want to log to console and file with different message formats and in differing circumstances. Say you want to log messages with levels of DEBUG and higher to file, and those messages at level INFO and higher to the console. Let’s also assume that the file should contain timestamps, but the console messages should not. Here’s how you can achieve this:
import logging
# set up logging to file - see previous section for more details
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M',
filename='/temp/myapp.log',
filemode='w')
# define a Handler which writes INFO messages or higher to the sys.stderr
console = logging.StreamHandler()
console.setLevel(logging.INFO)
# set a format which is simpler for console use
formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s')
# tell the handler to use this format
console.setFormatter(formatter)
# add the handler to the root logger
logging.getLogger('').addHandler(console)
# Now, we can log to the root logger, or any other logger. First the root...
logging.info('Jackdaws love my big sphinx of quartz.')
# Now, define a couple of other loggers which might represent areas in your
# application:
logger1 = logging.getLogger('myapp.area1')
logger2 = logging.getLogger('myapp.area2')
logger1.debug('Quick zephyrs blow, vexing daft Jim.')
logger1.info('How quickly daft jumping zebras vex.')
logger2.warning('Jail zesty vixen who grabbed pay from quack.')
logger2.error('The five boxing wizards jump quickly.')
When you run this, on the console you will see
root : INFO Jackdaws love my big sphinx of quartz.
myapp.area1 : INFO How quickly daft jumping zebras vex.
myapp.area2 : WARNING Jail zesty vixen who grabbed pay from quack.
myapp.area2 : ERROR The five boxing wizards jump quickly.
and in the file you will see something like
10-22 22:19 root INFO Jackdaws love my big sphinx of quartz.
10-22 22:19 myapp.area1 DEBUG Quick zephyrs blow, vexing daft Jim.
10-22 22:19 myapp.area1 INFO How quickly daft jumping zebras vex.
10-22 22:19 myapp.area2 WARNING Jail zesty vixen who grabbed pay from quack.
10-22 22:19 myapp.area2 ERROR The five boxing wizards jump quickly.
As you can see, the DEBUG message only shows up in the file. The other messages are sent to both destinations.
This example uses console and file handlers, but you can use any number and combination of handlers you choose.
The logging package is designed to swallow exceptions which occur while logging in production. This is so that errors which occur while handling logging events - such as logging misconfiguration, network or other similar errors - do not cause the application using logging to terminate prematurely.
SystemExit and KeyboardInterrupt exceptions are never swallowed. Other exceptions which occur during the emit() method of a Handler subclass are passed to its handleError() method.
The default implementation of handleError() in Handler checks to see if a module-level variable, raiseExceptions, is set. If set, a traceback is printed to sys.stderr. If not set, the exception is swallowed.
Note: The default value of raiseExceptions is True. This is because during development, you typically want to be notified of any exceptions that occur. It’s advised that you set raiseExceptions to False for production usage.
Sometimes you want logging output to contain contextual information in addition to the parameters passed to the logging call. For example, in a networked application, it may be desirable to log client-specific information in the log (e.g. remote client’s username, or IP address). Although you could use the extra parameter to achieve this, it’s not always convenient to pass the information in this way. While it might be tempting to create Logger instances on a per-connection basis, this is not a good idea because these instances are not garbage collected. While this is not a problem in practice, when the number of Logger instances is dependent on the level of granularity you want to use in logging an application, it could be hard to manage if the number of Logger instances becomes effectively unbounded.
An easy way in which you can pass contextual information to be output along with logging event information is to use the LoggerAdapter class. This class is designed to look like a Logger, so that you can call debug(), info(), warning(), error(), exception(), critical() and log(). These methods have the same signatures as their counterparts in Logger, so you can use the two types of instances interchangeably.
When you create an instance of LoggerAdapter, you pass it a Logger instance and a dict-like object which contains your contextual information. When you call one of the logging methods on an instance of LoggerAdapter, it delegates the call to the underlying instance of Logger passed to its constructor, and arranges to pass the contextual information in the delegated call. Here’s a snippet from the code of LoggerAdapter:
def debug(self, msg, *args, **kwargs):
"""
Delegate a debug call to the underlying logger, after adding
contextual information from this adapter instance.
"""
msg, kwargs = self.process(msg, kwargs)
self.logger.debug(msg, *args, **kwargs)
The process() method of LoggerAdapter is where the contextual information is added to the logging output. It’s passed the message and keyword arguments of the logging call, and it passes back (potentially) modified versions of these to use in the call to the underlying logger. The default implementation of this method leaves the message alone, but inserts an “extra” key in the keyword argument whose value is the dict-like object passed to the constructor. Of course, if you had passed an “extra” keyword argument in the call to the adapter, it will be silently overwritten.
The advantage of using “extra” is that the values in the dict-like object are merged into the LogRecord instance’s __dict__, allowing you to use customized strings with your Formatter instances which know about the keys of the dict-like object. If you need a different method, e.g. if you want to prepend or append the contextual information to the message string, you just need to subclass LoggerAdapter and override process() to do what you need. Here’s an example script which uses this class, which also illustrates what dict-like behaviour is needed from an arbitrary “dict-like” object for use in the constructor:
import logging
class ConnInfo:
"""
An example class which shows how an arbitrary class can be used as
the 'extra' context information repository passed to a LoggerAdapter.
"""
def __getitem__(self, name):
"""
To allow this instance to look like a dict.
"""
from random import choice
if name == "ip":
result = choice(["127.0.0.1", "192.168.0.1"])
elif name == "user":
result = choice(["jim", "fred", "sheila"])
else:
result = self.__dict__.get(name, "?")
return result
def __iter__(self):
"""
To allow iteration over keys, which will be merged into
the LogRecord dict before formatting and output.
"""
keys = ["ip", "user"]
keys.extend(self.__dict__.keys())
return keys.__iter__()
if __name__ == "__main__":
from random import choice
levels = (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL)
a1 = logging.LoggerAdapter(logging.getLogger("a.b.c"),
{ "ip" : "123.231.231.123", "user" : "sheila" })
logging.basicConfig(level=logging.DEBUG,
format="%(asctime)-15s %(name)-5s %(levelname)-8s IP: %(ip)-15s User: %(user)-8s %(message)s")
a1.debug("A debug message")
a1.info("An info message with %s", "some parameters")
a2 = logging.LoggerAdapter(logging.getLogger("d.e.f"), ConnInfo())
for x in range(10):
lvl = choice(levels)
lvlname = logging.getLevelName(lvl)
a2.log(lvl, "A message at %s level with %d %s", lvlname, 2, "parameters")
When this script is run, the output should look something like this:
2008-01-18 14:49:54,023 a.b.c DEBUG IP: 123.231.231.123 User: sheila A debug message
2008-01-18 14:49:54,023 a.b.c INFO IP: 123.231.231.123 User: sheila An info message with some parameters
2008-01-18 14:49:54,023 d.e.f CRITICAL IP: 192.168.0.1 User: jim A message at CRITICAL level with 2 parameters
2008-01-18 14:49:54,033 d.e.f INFO IP: 192.168.0.1 User: jim A message at INFO level with 2 parameters
2008-01-18 14:49:54,033 d.e.f WARNING IP: 192.168.0.1 User: sheila A message at WARNING level with 2 parameters
2008-01-18 14:49:54,033 d.e.f ERROR IP: 127.0.0.1 User: fred A message at ERROR level with 2 parameters
2008-01-18 14:49:54,033 d.e.f ERROR IP: 127.0.0.1 User: sheila A message at ERROR level with 2 parameters
2008-01-18 14:49:54,033 d.e.f WARNING IP: 192.168.0.1 User: sheila A message at WARNING level with 2 parameters
2008-01-18 14:49:54,033 d.e.f WARNING IP: 192.168.0.1 User: jim A message at WARNING level with 2 parameters
2008-01-18 14:49:54,033 d.e.f INFO IP: 192.168.0.1 User: fred A message at INFO level with 2 parameters
2008-01-18 14:49:54,033 d.e.f WARNING IP: 192.168.0.1 User: sheila A message at WARNING level with 2 parameters
2008-01-18 14:49:54,033 d.e.f WARNING IP: 127.0.0.1 User: jim A message at WARNING level with 2 parameters
You can also add contextual information to log output using a user-defined Filter. Filter instances are allowed to modify the LogRecords passed to them, including adding additional attributes which can then be output using a suitable format string, or if needed a custom Formatter.
For example in a web application, the request being processed (or at least, the interesting parts of it) can be stored in a threadlocal (threading.local) variable, and then accessed from a Filter to add, say, information from the request - say, the remote IP address and remote user’s username - to the LogRecord, using the attribute names ‘ip’ and ‘user’ as in the LoggerAdapter example above. In that case, the same format string can be used to get similar output to that shown above. Here’s an example script:
import logging
from random import choice
class ContextFilter(logging.Filter):
"""
This is a filter which injects contextual information into the log.
Rather than use actual contextual information, we just use random
data in this demo.
"""
USERS = ['jim', 'fred', 'sheila']
IPS = ['123.231.231.123', '127.0.0.1', '192.168.0.1']
def filter(self, record):
record.ip = choice(ContextFilter.IPS)
record.user = choice(ContextFilter.USERS)
return True
if __name__ == "__main__":
levels = (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL)
a1 = logging.LoggerAdapter(logging.getLogger("a.b.c"),
{ "ip" : "123.231.231.123", "user" : "sheila" })
logging.basicConfig(level=logging.DEBUG,
format="%(asctime)-15s %(name)-5s %(levelname)-8s IP: %(ip)-15s User: %(user)-8s %(message)s")
a1 = logging.getLogger("a.b.c")
a2 = logging.getLogger("d.e.f")
f = ContextFilter()
a1.addFilter(f)
a2.addFilter(f)
a1.debug("A debug message")
a1.info("An info message with %s", "some parameters")
for x in range(10):
lvl = choice(levels)
lvlname = logging.getLevelName(lvl)
a2.log(lvl, "A message at %s level with %d %s", lvlname, 2, "parameters")
which, when run, produces something like:
2010-09-06 22:38:15,292 a.b.c DEBUG IP: 123.231.231.123 User: fred A debug message
2010-09-06 22:38:15,300 a.b.c INFO IP: 192.168.0.1 User: sheila An info message with some parameters
2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 127.0.0.1 User: sheila A message at CRITICAL level with 2 parameters
2010-09-06 22:38:15,300 d.e.f ERROR IP: 127.0.0.1 User: jim A message at ERROR level with 2 parameters
2010-09-06 22:38:15,300 d.e.f DEBUG IP: 127.0.0.1 User: sheila A message at DEBUG level with 2 parameters
2010-09-06 22:38:15,300 d.e.f ERROR IP: 123.231.231.123 User: fred A message at ERROR level with 2 parameters
2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 192.168.0.1 User: jim A message at CRITICAL level with 2 parameters
2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 127.0.0.1 User: sheila A message at CRITICAL level with 2 parameters
2010-09-06 22:38:15,300 d.e.f DEBUG IP: 192.168.0.1 User: jim A message at DEBUG level with 2 parameters
2010-09-06 22:38:15,301 d.e.f ERROR IP: 127.0.0.1 User: sheila A message at ERROR level with 2 parameters
2010-09-06 22:38:15,301 d.e.f DEBUG IP: 123.231.231.123 User: fred A message at DEBUG level with 2 parameters
2010-09-06 22:38:15,301 d.e.f INFO IP: 123.231.231.123 User: fred A message at INFO level with 2 parameters
Although logging is thread-safe, and logging to a single file from multiple threads in a single process is supported, logging to a single file from multiple processes is not supported, because there is no standard way to serialize access to a single file across multiple processes in Python. If you need to log to a single file from multiple processes, one way of doing this is to have all the processes log to a SocketHandler, and have a separate process which implements a socket server which reads from the socket and logs to file. (If you prefer, you can dedicate one thread in one of the existing processes to perform this function.) The following section documents this approach in more detail and includes a working socket receiver which can be used as a starting point for you to adapt in your own applications.
If you are using a recent version of Python which includes the multiprocessing module, you could write your own handler which uses the Lock class from this module to serialize access to the file from your processes. The existing FileHandler and subclasses do not make use of multiprocessing at present, though they may do so in the future. Note that at present, the multiprocessing module does not provide working lock functionality on all platforms (see http://bugs.python.org/issue3770).
Let’s say you want to send logging events across a network, and handle them at the receiving end. A simple way of doing this is attaching a SocketHandler instance to the root logger at the sending end:
import logging, logging.handlers
rootLogger = logging.getLogger('')
rootLogger.setLevel(logging.DEBUG)
socketHandler = logging.handlers.SocketHandler('localhost',
logging.handlers.DEFAULT_TCP_LOGGING_PORT)
# don't bother with a formatter, since a socket handler sends the event as
# an unformatted pickle
rootLogger.addHandler(socketHandler)
# Now, we can log to the root logger, or any other logger. First the root...
logging.info('Jackdaws love my big sphinx of quartz.')
# Now, define a couple of other loggers which might represent areas in your
# application:
logger1 = logging.getLogger('myapp.area1')
logger2 = logging.getLogger('myapp.area2')
logger1.debug('Quick zephyrs blow, vexing daft Jim.')
logger1.info('How quickly daft jumping zebras vex.')
logger2.warning('Jail zesty vixen who grabbed pay from quack.')
logger2.error('The five boxing wizards jump quickly.')
At the receiving end, you can set up a receiver using the socketserver module. Here is a basic working example:
import pickle
import logging
import logging.handlers
import socketserver
import struct
class LogRecordStreamHandler(socketserver.StreamRequestHandler):
"""Handler for a streaming logging request.
This basically logs the record using whatever logging policy is
configured locally.
"""
def handle(self):
"""
Handle multiple requests - each expected to be a 4-byte length,
followed by the LogRecord in pickle format. Logs the record
according to whatever policy is configured locally.
"""
while True:
chunk = self.connection.recv(4)
if len(chunk) < 4:
break
slen = struct.unpack(">L", chunk)[0]
chunk = self.connection.recv(slen)
while len(chunk) < slen:
chunk = chunk + self.connection.recv(slen - len(chunk))
obj = self.unPickle(chunk)
record = logging.makeLogRecord(obj)
self.handleLogRecord(record)
def unPickle(self, data):
return pickle.loads(data)
def handleLogRecord(self, record):
# if a name is specified, we use the named logger rather than the one
# implied by the record.
if self.server.logname is not None:
name = self.server.logname
else:
name = record.name
logger = logging.getLogger(name)
# N.B. EVERY record gets logged. This is because Logger.handle
# is normally called AFTER logger-level filtering. If you want
# to do filtering, do it at the client end to save wasting
# cycles and network bandwidth!
logger.handle(record)
class LogRecordSocketReceiver(socketserver.ThreadingTCPServer):
"""simple TCP socket-based logging receiver suitable for testing.
"""
allow_reuse_address = 1
def __init__(self, host='localhost',
port=logging.handlers.DEFAULT_TCP_LOGGING_PORT,
handler=LogRecordStreamHandler):
socketserver.ThreadingTCPServer.__init__(self, (host, port), handler)
self.abort = 0
self.timeout = 1
self.logname = None
def serve_until_stopped(self):
import select
abort = 0
while not abort:
rd, wr, ex = select.select([self.socket.fileno()],
[], [],
self.timeout)
if rd:
self.handle_request()
abort = self.abort
def main():
logging.basicConfig(
format="%(relativeCreated)5d %(name)-15s %(levelname)-8s %(message)s")
tcpserver = LogRecordSocketReceiver()
print("About to start TCP server...")
tcpserver.serve_until_stopped()
if __name__ == "__main__":
main()
First run the server, and then the client. On the client side, nothing is printed on the console; on the server side, you should see something like:
About to start TCP server...
59 root INFO Jackdaws love my big sphinx of quartz.
59 myapp.area1 DEBUG Quick zephyrs blow, vexing daft Jim.
69 myapp.area1 INFO How quickly daft jumping zebras vex.
69 myapp.area2 WARNING Jail zesty vixen who grabbed pay from quack.
69 myapp.area2 ERROR The five boxing wizards jump quickly.
Note that there are some security issues with pickle in some scenarios. If these affect you, you can use an alternative serialization scheme by overriding the makePickle() method and implementing your alternative there, as well as adapting the above script to use your alternative serialization.
In the preceding sections and examples, it has been assumed that the message passed when logging the event is a string. However, this is not the only possibility. You can pass an arbitrary object as a message, and its __str__() method will be called when the logging system needs to convert it to a string representation. In fact, if you want to, you can avoid computing a string representation altogether - for example, the SocketHandler emits an event by pickling it and sending it over the wire.
Formatting of message arguments is deferred until it cannot be avoided. However, computing the arguments passed to the logging method can also be expensive, and you may want to avoid doing it if the logger will just throw away your event. To decide what to do, you can call the isEnabledFor() method which takes a level argument and returns true if the event would be created by the Logger for that level of call. You can write code like this:
if logger.isEnabledFor(logging.DEBUG):
logger.debug("Message with %s, %s", expensive_func1(),
expensive_func2())
so that if the logger’s threshold is set above DEBUG, the calls to expensive_func1() and expensive_func2() are never made.
There are other optimizations which can be made for specific applications which need more precise control over what logging information is collected. Here’s a list of things you can do to avoid processing during logging which you don’t need:
| What you don’t want to collect | How to avoid collecting it |
|---|---|
| Information about where calls were made from. | Set logging._srcfile to None. |
| Threading information. | Set logging.logThreads to 0. |
| Process information. | Set logging.logProcesses to 0. |
Also note that the core logging module only includes the basic handlers. If you don’t import logging.handlers and logging.config, they won’t take up any memory.
Handlers have the following attributes and methods. Note that Handler is never instantiated directly; this class acts as a base for more useful subclasses. However, the __init__() method in subclasses needs to call Handler.__init__().
The StreamHandler class, located in the core logging package, sends logging output to streams such as sys.stdout, sys.stderr or any file-like object (or, more precisely, any object which supports write() and flush() methods).
Returns a new instance of the StreamHandler class. If stream is specified, the instance will use it for logging output; otherwise, sys.stderr will be used.
The FileHandler class, located in the core logging package, sends logging output to a disk file. It inherits the output functionality from StreamHandler.
Returns a new instance of the FileHandler class. The specified file is opened and used as the stream for logging. If mode is not specified, 'a' is used. If encoding is not None, it is used to open the file with that encoding. If delay is true, then file opening is deferred until the first call to emit(). By default, the file grows indefinitely.
New in version 3.1.
The NullHandler class, located in the core logging package, does not do any formatting or output. It is essentially a “no-op” handler for use by library developers.
Returns a new instance of the NullHandler class.
See Configuring Logging for a Library for more information on how to use NullHandler.
The WatchedFileHandler class, located in the logging.handlers module, is a FileHandler which watches the file it is logging to. If the file changes, it is closed and reopened using the file name.
A file change can happen because of usage of programs such as newsyslog and logrotate which perform log file rotation. This handler, intended for use under Unix/Linux, watches the file to see if it has changed since the last emit. (A file is deemed to have changed if its device or inode have changed.) If the file has changed, the old file stream is closed, and the file opened to get a new stream.
This handler is not appropriate for use under Windows, because under Windows open log files cannot be moved or renamed - logging opens the files with exclusive locks - and so there is no need for such a handler. Furthermore, ST_INO is not supported under Windows; stat() always returns zero for this value.
Returns a new instance of the WatchedFileHandler class. The specified file is opened and used as the stream for logging. If mode is not specified, 'a' is used. If encoding is not None, it is used to open the file with that encoding. If delay is true, then file opening is deferred until the first call to emit(). By default, the file grows indefinitely.
The RotatingFileHandler class, located in the logging.handlers module, supports rotation of disk log files.
Returns a new instance of the RotatingFileHandler class. The specified file is opened and used as the stream for logging. If mode is not specified, 'a' is used. If encoding is not None, it is used to open the file with that encoding. If delay is true, then file opening is deferred until the first call to emit(). By default, the file grows indefinitely.
You can use the maxBytes and backupCount values to allow the file to rollover at a predetermined size. When the size is about to be exceeded, the file is closed and a new file is silently opened for output. Rollover occurs whenever the current log file is nearly maxBytes in length; if maxBytes is zero, rollover never occurs. If backupCount is non-zero, the system will save old log files by appending the extensions “.1”, “.2” etc., to the filename. For example, with a backupCount of 5 and a base file name of app.log, you would get app.log, app.log.1, app.log.2, up to app.log.5. The file being written to is always app.log. When this file is filled, it is closed and renamed to app.log.1, and if files app.log.1, app.log.2, etc. exist, then they are renamed to app.log.2, app.log.3 etc. respectively.
The TimedRotatingFileHandler class, located in the logging.handlers module, supports rotation of disk log files at certain timed intervals.
Returns a new instance of the TimedRotatingFileHandler class. The specified file is opened and used as the stream for logging. On rotating it also sets the filename suffix. Rotating happens based on the product of when and interval.
You can use the when to specify the type of interval. The list of possible values is below. Note that they are not case sensitive.
| Value | Type of interval |
|---|---|
| 'S' | Seconds |
| 'M' | Minutes |
| 'H' | Hours |
| 'D' | Days |
| 'W' | Week day (0=Monday) |
| 'midnight' | Roll over at midnight |
The system will save old log files by appending extensions to the filename. The extensions are date-and-time based, using the strftime format %Y-%m-%d_%H-%M-%S or a leading portion thereof, depending on the rollover interval.
When computing the next rollover time for the first time (when the handler is created), the last modification time of an existing log file, or else the current time, is used to compute when the next rotation will occur.
If the utc argument is true, times in UTC will be used; otherwise local time is used.
If backupCount is nonzero, at most backupCount files will be kept, and if more would be created when rollover occurs, the oldest one is deleted. The deletion logic uses the interval to determine which files to delete, so changing the interval may leave old files lying around.
If delay is true, then file opening is deferred until the first call to emit().
The SocketHandler class, located in the logging.handlers module, sends logging output to a network socket. The base class uses a TCP socket.
Returns a new instance of the SocketHandler class intended to communicate with a remote machine whose address is given by host and port.
Pickles the record’s attribute dictionary in binary format with a length prefix, and returns it ready for transmission across the socket.
Note that pickles aren’t completely secure. If you are concerned about security, you may want to override this method to implement a more secure mechanism. For example, you can sign pickles using HMAC and then verify them on the receiving end, or alternatively you can disable unpickling of global objects on the receiving end.
The DatagramHandler class, located in the logging.handlers module, inherits from SocketHandler to support sending logging messages over UDP sockets.
Returns a new instance of the DatagramHandler class intended to communicate with a remote machine whose address is given by host and port.
The SysLogHandler class, located in the logging.handlers module, supports sending logging messages to a remote or local Unix syslog.
Returns a new instance of the SysLogHandler class intended to communicate with a remote Unix machine whose address is given by address in the form of a (host, port) tuple. If address is not specified, ('localhost', 514) is used. The address is used to open a UDP socket. An alternative to providing a (host, port) tuple is providing an address as a string, for example “/dev/log”. In this case, a Unix domain socket is used to send the message to the syslog. If facility is not specified, LOG_USER is used.
Encodes the facility and priority into an integer. You can pass in strings or integers - if strings are passed, internal mapping dictionaries are used to convert them to integers.
The symbolic LOG_ values are defined in SysLogHandler and mirror the values defined in the sys/syslog.h header file.
Priorities
| Name (string) | Symbolic value |
|---|---|
| alert | LOG_ALERT |
| crit or critical | LOG_CRIT |
| debug | LOG_DEBUG |
| emerg or panic | LOG_EMERG |
| err or error | LOG_ERR |
| info | LOG_INFO |
| notice | LOG_NOTICE |
| warn or warning | LOG_WARNING |
Facilities
| Name (string) | Symbolic value |
|---|---|
| auth | LOG_AUTH |
| authpriv | LOG_AUTHPRIV |
| cron | LOG_CRON |
| daemon | LOG_DAEMON |
| ftp | LOG_FTP |
| kern | LOG_KERN |
| lpr | LOG_LPR |
| LOG_MAIL | |
| news | LOG_NEWS |
| syslog | LOG_SYSLOG |
| user | LOG_USER |
| uucp | LOG_UUCP |
| local0 | LOG_LOCAL0 |
| local1 | LOG_LOCAL1 |
| local2 | LOG_LOCAL2 |
| local3 | LOG_LOCAL3 |
| local4 | LOG_LOCAL4 |
| local5 | LOG_LOCAL5 |
| local6 | LOG_LOCAL6 |
| local7 | LOG_LOCAL7 |
The NTEventLogHandler class, located in the logging.handlers module, supports sending logging messages to a local Windows NT, Windows 2000 or Windows XP event log. Before you can use it, you need Mark Hammond’s Win32 extensions for Python installed.
Returns a new instance of the NTEventLogHandler class. The appname is used to define the application name as it appears in the event log. An appropriate registry entry is created using this name. The dllname should give the fully qualified pathname of a .dll or .exe which contains message definitions to hold in the log (if not specified, 'win32service.pyd' is used - this is installed with the Win32 extensions and contains some basic placeholder message definitions. Note that use of these placeholders will make your event logs big, as the entire message source is held in the log. If you want slimmer logs, you have to pass in the name of your own .dll or .exe which contains the message definitions you want to use in the event log). The logtype is one of 'Application', 'System' or 'Security', and defaults to 'Application'.
The SMTPHandler class, located in the logging.handlers module, supports sending logging messages to an email address via SMTP.
Returns a new instance of the SMTPHandler class. The instance is initialized with the from and to addresses and subject line of the email. The toaddrs should be a list of strings. To specify a non-standard SMTP port, use the (host, port) tuple format for the mailhost argument. If you use a string, the standard SMTP port is used. If your SMTP server requires authentication, you can specify a (username, password) tuple for the credentials argument.
The MemoryHandler class, located in the logging.handlers module, supports buffering of logging records in memory, periodically flushing them to a target handler. Flushing occurs whenever the buffer is full, or when an event of a certain severity or greater is seen.
MemoryHandler is a subclass of the more general BufferingHandler, which is an abstract class. This buffers logging records in memory. Whenever each record is added to the buffer, a check is made by calling shouldFlush() to see if the buffer should be flushed. If it should, then flush() is expected to do the needful.
Initializes the handler with a buffer of the specified capacity.
Returns a new instance of the MemoryHandler class. The instance is initialized with a buffer size of capacity. If flushLevel is not specified, ERROR is used. If no target is specified, the target will need to be set using setTarget() before this handler does anything useful.
The HTTPHandler class, located in the logging.handlers module, supports sending logging messages to a Web server, using either GET or POST semantics.
Returns a new instance of the HTTPHandler class. The instance is initialized with a host address, url and HTTP method. The host can be of the form host:port, should you need to use a specific port number. If no method is specified, GET is used.
Formatters have the following attributes and methods. They are responsible for converting a LogRecord to (usually) a string which can be interpreted by either a human or an external system. The base Formatter allows a formatting string to be specified. If none is supplied, the default value of '%(message)s' is used.
A Formatter can be initialized with a format string which makes use of knowledge of the LogRecord attributes - such as the default value mentioned above making use of the fact that the user’s message and arguments are pre-formatted into a LogRecord‘s message attribute. This format string contains standard Python %-style mapping keys. See section Old String Formatting Operations for more information on string formatting.
Currently, the useful mapping keys in a LogRecord are:
| Format | Description |
|---|---|
| %(name)s | Name of the logger (logging channel). |
| %(levelno)s | Numeric logging level for the message (DEBUG, INFO, WARNING, ERROR, CRITICAL). |
| %(levelname)s | Text logging level for the message ('DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'). |
| %(pathname)s | Full pathname of the source file where the logging call was issued (if available). |
| %(filename)s | Filename portion of pathname. |
| %(module)s | Module (name portion of filename). |
| %(funcName)s | Name of function containing the logging call. |
| %(lineno)d | Source line number where the logging call was issued (if available). |
| %(created)f | Time when the LogRecord was created (as returned by time.time()). |
| %(relativeCreated)d | Time in milliseconds when the LogRecord was created, relative to the time the logging module was loaded. |
| %(asctime)s | Human-readable time when the LogRecord was created. By default this is of the form “2003-07-08 16:49:45,896” (the numbers after the comma are millisecond portion of the time). |
| %(msecs)d | Millisecond portion of the time when the LogRecord was created. |
| %(thread)d | Thread ID (if available). |
| %(threadName)s | Thread name (if available). |
| %(process)d | Process ID (if available). |
| %(processName)s | Process name (if available). |
| %(message)s | The logged message, computed as msg % args. |
Returns a new instance of the Formatter class. The instance is initialized with a format string for the message as a whole, as well as a format string for the date/time portion of a message. If no fmt is specified, '%(message)s' is used. If no datefmt is specified, the ISO8601 date format is used.
Filters can be used by Handlers and Loggers for more sophisticated filtering than is provided by levels. The base filter class only allows events which are below a certain point in the logger hierarchy. For example, a filter initialized with “A.B” will allow events logged by loggers “A.B”, “A.B.C”, “A.B.C.D”, “A.B.D” etc. but not “A.BB”, “B.A.B” etc. If initialized with the empty string, all events are passed.
Returns an instance of the Filter class. If name is specified, it names a logger which, together with its children, will have its events allowed through the filter. If name is the empty string, allows every event.
Note that filters attached to handlers are consulted whenever an event is emitted by the handler, whereas filters attached to loggers are consulted whenever an event is logged to the handler (using debug(), info(), etc.) This means that events which have been generated by descendant loggers will not be filtered by a logger’s filter setting, unless the filter has also been applied to those descendant loggers.
LogRecord instances are created automatically by the Logger every time something is logged, and can be created manually via makeLogRecord() (for example, from a pickled event received over the wire).
Contains all the information pertinent to the event being logged.
The primary information is passed in msg and args, which are combined using msg % args to create the message field of the record.
LoggerAdapter instances are used to conveniently pass contextual information into logging calls. For a usage example , see the section on adding contextual information to your logging output.
Returns an instance of LoggerAdapter initialized with an underlying Logger instance and a dict-like object.
In addition to the above, LoggerAdapter supports all the logging methods of Logger, i.e. debug(), info(), warning(), error(), exception(), critical() and log(). These methods have the same signatures as their counterparts in Logger, so you can use the two types of instances interchangeably.
The isEnabledFor() method was added to LoggerAdapter. This method delegates to the underlying logger.
The logging module is intended to be thread-safe without any special work needing to be done by its clients. It achieves this though using threading locks; there is one lock to serialize access to the module’s shared data, and each handler also creates a lock to serialize access to its underlying I/O.
If you are implementing asynchronous signal handlers using the signal module, you may not be able to use logging from within such handlers. This is because lock implementations in the threading module are not always re-entrant, and so cannot be invoked from such signal handlers.
The following functions configure the logging module. They are located in the logging.config module. Their use is optional — you can configure the logging module using these functions or by making calls to the main API (defined in logging itself) and defining handlers which are declared either in logging or logging.handlers.
Reads the logging configuration from a configparser-format file named fname. This function can be called several times from an application, allowing an end user the ability to select from various pre-canned configurations (if the developer provides a mechanism to present the choices and load the chosen configuration). Defaults to be passed to the ConfigParser can be specified in the defaults argument.
If disable_existing_loggers is true, any existing loggers that are not children of named loggers will be disabled.
Starts up a socket server on the specified port, and listens for new configurations. If no port is specified, the module’s default DEFAULT_LOGGING_CONFIG_PORT is used. Logging configurations will be sent as a file suitable for processing by fileConfig(). Returns a Thread instance on which you can call start() to start the server, and which you can join() when appropriate. To stop the server, call stopListening().
To send a configuration to the socket, read in the configuration file and send it to the socket as a string of bytes preceded by a four-byte length string packed in binary using struct.pack('>L', n).
The configuration file format understood by fileConfig() is based on configparser functionality. The file must contain sections called [loggers], [handlers] and [formatters] which identify by name the entities of each type which are defined in the file. For each such entity, there is a separate section which identifies how that entity is configured. Thus, for a logger named log01 in the [loggers] section, the relevant configuration details are held in a section [logger_log01]. Similarly, a handler called hand01 in the [handlers] section will have its configuration held in a section called [handler_hand01], while a formatter called form01 in the [formatters] section will have its configuration specified in a section called [formatter_form01]. The root logger configuration must be specified in a section called [logger_root].
Examples of these sections in the file are given below.
[loggers]
keys=root,log02,log03,log04,log05,log06,log07
[handlers]
keys=hand01,hand02,hand03,hand04,hand05,hand06,hand07,hand08,hand09
[formatters]
keys=form01,form02,form03,form04,form05,form06,form07,form08,form09
The root logger must specify a level and a list of handlers. An example of a root logger section is given below.
[logger_root]
level=NOTSET
handlers=hand01
The level entry can be one of DEBUG, INFO, WARNING, ERROR, CRITICAL or NOTSET. For the root logger only, NOTSET means that all messages will be logged. Level values are eval()uated in the context of the logging package’s namespace.
The handlers entry is a comma-separated list of handler names, which must appear in the [handlers] section. These names must appear in the [handlers] section and have corresponding sections in the configuration file.
For loggers other than the root logger, some additional information is required. This is illustrated by the following example.
[logger_parser]
level=DEBUG
handlers=hand01
propagate=1
qualname=compiler.parser
The level and handlers entries are interpreted as for the root logger, except that if a non-root logger’s level is specified as NOTSET, the system consults loggers higher up the hierarchy to determine the effective level of the logger. The propagate entry is set to 1 to indicate that messages must propagate to handlers higher up the logger hierarchy from this logger, or 0 to indicate that messages are not propagated to handlers up the hierarchy. The qualname entry is the hierarchical channel name of the logger, that is to say the name used by the application to get the logger.
Sections which specify handler configuration are exemplified by the following.
[handler_hand01]
class=StreamHandler
level=NOTSET
formatter=form01
args=(sys.stdout,)
The class entry indicates the handler’s class (as determined by eval() in the logging package’s namespace). The level is interpreted as for loggers, and NOTSET is taken to mean “log everything”.
The formatter entry indicates the key name of the formatter for this handler. If blank, a default formatter (logging._defaultFormatter) is used. If a name is specified, it must appear in the [formatters] section and have a corresponding section in the configuration file.
The args entry, when eval()uated in the context of the logging package’s namespace, is the list of arguments to the constructor for the handler class. Refer to the constructors for the relevant handlers, or to the examples below, to see how typical entries are constructed.
[handler_hand02]
class=FileHandler
level=DEBUG
formatter=form02
args=('python.log', 'w')
[handler_hand03]
class=handlers.SocketHandler
level=INFO
formatter=form03
args=('localhost', handlers.DEFAULT_TCP_LOGGING_PORT)
[handler_hand04]
class=handlers.DatagramHandler
level=WARN
formatter=form04
args=('localhost', handlers.DEFAULT_UDP_LOGGING_PORT)
[handler_hand05]
class=handlers.SysLogHandler
level=ERROR
formatter=form05
args=(('localhost', handlers.SYSLOG_UDP_PORT), handlers.SysLogHandler.LOG_USER)
[handler_hand06]
class=handlers.NTEventLogHandler
level=CRITICAL
formatter=form06
args=('Python Application', '', 'Application')
[handler_hand07]
class=handlers.SMTPHandler
level=WARN
formatter=form07
args=('localhost', 'from@abc', ['user1@abc', 'user2@xyz'], 'Logger Subject')
[handler_hand08]
class=handlers.MemoryHandler
level=NOTSET
formatter=form08
target=
args=(10, ERROR)
[handler_hand09]
class=handlers.HTTPHandler
level=NOTSET
formatter=form09
args=('localhost:9022', '/log', 'GET')
Sections which specify formatter configuration are typified by the following.
[formatter_form01]
format=F1 %(asctime)s %(levelname)s %(message)s
datefmt=
class=logging.Formatter
The format entry is the overall format string, and the datefmt entry is the strftime()-compatible date/time format string. If empty, the package substitutes ISO8601 format date/times, which is almost equivalent to specifying the date format string "%Y-%m-%d %H:%M:%S". The ISO8601 format also specifies milliseconds, which are appended to the result of using the above format string, with a comma separator. An example time in ISO8601 format is 2003-01-23 00:29:50,411.
The class entry is optional. It indicates the name of the formatter’s class (as a dotted module and class name.) This option is useful for instantiating a Formatter subclass. Subclasses of Formatter can present exception tracebacks in an expanded or condensed format.
Here is an example of a module using the logging configuration server:
import logging
import logging.config
import time
import os
# read initial config file
logging.config.fileConfig("logging.conf")
# create and start listener on port 9999
t = logging.config.listen(9999)
t.start()
logger = logging.getLogger("simpleExample")
try:
# loop through logging calls to see the difference
# new configurations make, until Ctrl+C is pressed
while True:
logger.debug("debug message")
logger.info("info message")
logger.warn("warn message")
logger.error("error message")
logger.critical("critical message")
time.sleep(5)
except KeyboardInterrupt:
# cleanup
logging.config.stopListening()
t.join()
And here is a script that takes a filename and sends that file to the server, properly preceded with the binary-encoded length, as the new logging configuration:
#!/usr/bin/env python
import socket, sys, struct
data_to_send = open(sys.argv[1], "r").read()
HOST = 'localhost'
PORT = 9999
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
print("connecting...")
s.connect((HOST, PORT))
print("sending config...")
s.send(struct.pack(">L", len(data_to_send)))
s.send(data_to_send)
s.close()
print("complete")
Loggers are plain Python objects. The addHandler() method has no minimum or maximum quota for the number of handlers you may add. Sometimes it will be beneficial for an application to log all messages of all severities to a text file while simultaneously logging errors or above to the console. To set this up, simply configure the appropriate handlers. The logging calls in the application code will remain unchanged. Here is a slight modification to the previous simple module-based configuration example:
import logging
logger = logging.getLogger("simple_example")
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
fh = logging.FileHandler("spam.log")
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
# create formatter and add it to the handlers
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
ch.setFormatter(formatter)
fh.setFormatter(formatter)
# add the handlers to logger
logger.addHandler(ch)
logger.addHandler(fh)
# "application" code
logger.debug("debug message")
logger.info("info message")
logger.warn("warn message")
logger.error("error message")
logger.critical("critical message")
Notice that the “application” code does not care about multiple handlers. All that changed was the addition and configuration of a new handler named fh.
The ability to create new handlers with higher- or lower-severity filters can be very helpful when writing and testing an application. Instead of using many print statements for debugging, use logger.debug: Unlike the print statements, which you will have to delete or comment out later, the logger.debug statements can remain intact in the source code and remain dormant until you need them again. At that time, the only change that needs to happen is to modify the severity level of the logger and/or handler to debug.
It was mentioned above that multiple calls to logging.getLogger('someLogger') return a reference to the same logger object. This is true not only within the same module, but also across modules as long as it is in the same Python interpreter process. It is true for references to the same object; additionally, application code can define and configure a parent logger in one module and create (but not configure) a child logger in a separate module, and all logger calls to the child will pass up to the parent. Here is a main module:
import logging
import auxiliary_module
# create logger with "spam_application"
logger = logging.getLogger("spam_application")
logger.setLevel(logging.DEBUG)
# create file handler which logs even debug messages
fh = logging.FileHandler("spam.log")
fh.setLevel(logging.DEBUG)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.ERROR)
# create formatter and add it to the handlers
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
fh.setFormatter(formatter)
ch.setFormatter(formatter)
# add the handlers to the logger
logger.addHandler(fh)
logger.addHandler(ch)
logger.info("creating an instance of auxiliary_module.Auxiliary")
a = auxiliary_module.Auxiliary()
logger.info("created an instance of auxiliary_module.Auxiliary")
logger.info("calling auxiliary_module.Auxiliary.do_something")
a.do_something()
logger.info("finished auxiliary_module.Auxiliary.do_something")
logger.info("calling auxiliary_module.some_function()")
auxiliary_module.some_function()
logger.info("done with auxiliary_module.some_function()")
Here is the auxiliary module:
import logging
# create logger
module_logger = logging.getLogger("spam_application.auxiliary")
class Auxiliary:
def __init__(self):
self.logger = logging.getLogger("spam_application.auxiliary.Auxiliary")
self.logger.info("creating an instance of Auxiliary")
def do_something(self):
self.logger.info("doing something")
a = 1 + 1
self.logger.info("done doing something")
def some_function():
module_logger.info("received a call to \"some_function\"")
The output looks like this:
2005-03-23 23:47:11,663 - spam_application - INFO -
creating an instance of auxiliary_module.Auxiliary
2005-03-23 23:47:11,665 - spam_application.auxiliary.Auxiliary - INFO -
creating an instance of Auxiliary
2005-03-23 23:47:11,665 - spam_application - INFO -
created an instance of auxiliary_module.Auxiliary
2005-03-23 23:47:11,668 - spam_application - INFO -
calling auxiliary_module.Auxiliary.do_something
2005-03-23 23:47:11,668 - spam_application.auxiliary.Auxiliary - INFO -
doing something
2005-03-23 23:47:11,669 - spam_application.auxiliary.Auxiliary - INFO -
done doing something
2005-03-23 23:47:11,670 - spam_application - INFO -
finished auxiliary_module.Auxiliary.do_something
2005-03-23 23:47:11,671 - spam_application - INFO -
calling auxiliary_module.some_function()
2005-03-23 23:47:11,672 - spam_application.auxiliary - INFO -
received a call to "some_function"
2005-03-23 23:47:11,673 - spam_application - INFO -
done with auxiliary_module.some_function()